1. Field of the Invention
The present invention is a system, method, and apparatus for determining the hierarchical purchase decision process (consumer decision trees) of consumers in front of a product category (a set of products that meet the same need of consumers), which is organized in a coordinate system either as brand or product attribute blocks, in a retail store, wherein shopping consideration and the decision path of consumers is obtained by combining behavioral data with the category layout and transactional data.
2. Background of the Invention
U.S. Pat. No. 7,130,836 of Grosser, et al. (hereinafter Grosser) disclosed an apparatus and method for a computer-aided decision-making system. Grosser provides assistance and feedback to a person in a decision-making context that helps the person to evaluate and rank various choices based on criteria he or she provides. Grosser is intended for consumer decision making in an online environment, such as the Internet. For example, Grosser used buying a home or buying a car online as the examples of applications.
Although Grosser claims that their system can be used for various decision-making applications, Grosser is foreign to the idea of automatically determining the hierarchical purchase decision process of shoppers in front of a product category in the retail store based on observation of the physical shopping behavior. Especially, Grosser is foreign to the idea of obtaining the shopping decision path of shoppers by combining behavioral data in images processed by video analytics with the category layout and transactional data.
Grosser is also a decision support system, whereas the present invention is not necessarily intended for aiding the decision process of shoppers. One of the objectives of the present invention in a preferred embodiment is to automatically determine the decision process of shoppers without any involvement from the shoppers in the physical space. The present invention obtains unaided shopper behavior from the described apparatus, and helps retailers and manufacturers to match the category layout based on the shopper's decision process, thus enabling a convenient shopping experience.
U.S. Pat. No. 7,076,456 of Rofrano (hereinafter Rofrano) disclosed a system that utilizes question and answer trees. In Rofrano, a potential buyer can select from a pre-ranked list of questions and then follow the answers in a decision tree-type way to the final product choice/answer. The questions are ranked according to importance, which is based on the various factors, including the past frequencies asked by other buyers, an order of questioning that would normally be asked by a salesperson, and statistical data based on the effectiveness of past questions. The system in Rofrano essentially takes the place of an actual salesperson, and is designed for developing an electronic catalog in which the search can be customized based on the customer's preferences.
The system disclosed by Rofrano applies only to purchasing from an electronic catalog and cannot be employed for tracking the behavior of shoppers in a retail environment.
U.S. Pat. No. 5,331,544 of Lu, et al. (hereinafter Lu) disclosed an exemplary automated system for collecting market research data. In Lu, an automatic face recognition system is disclosed to identify a customer in a retail environment, which can measure the shopping frequency at a given store. Lu also noted that a shopper's attentiveness to a display may be correlated with purchases of products and with other demographic purchase-related variables.
However, Lu is foreign to the idea of analyzing shopper behavior based on movement in a retail store. Furthermore, Lu is entirely foreign to the idea of obtaining the shopping decision path and tree based on the shopping behavior. Lu's system can be used only to identify frequent shoppers by matching facial images of the shopper, and to guess that a product purchase was possibly the result of the shopper viewing the display. However, Lu cannot be used to determine the hierarchical decision process of shoppers, as outlined in the present invention.
U.S. Pat. No. 5,974,396 of Anderson, et al. (hereinafter Anderson) disclosed a system for gathering and analyzing customer purchasing information based on the relationship between product and consumer clustering. In Anderson, product information is gathered by type and manufacturer, and the products are grouped into product clusters. Consumers are grouped into consumer clusters based on common consumer demographics and other characteristics. Consumer retail transactions are analyzed in terms of the product and consumer clusters, and the relationships between the consumers and the products are determined by retailers. Anderson enables the retailers to target marketing materials toward specific consumer groups based on their buying habits, needs, and demographics, and to make business decisions based on the queried information from the product and consumer relationship database.
Anderson is also foreign to the idea of obtaining the shopping decision path of shoppers based on behavioral data in images processed by video analytics and constructing a decision tree based on the accumulated decision paths. Further, Anderson can only determine the buying patterns of consumers, and cannot identify the decision-making process. Understanding the hierarchical decision-making process and correlating it with the purchase data, as detailed by the present invention, can help to identify growth opportunities; this cannot be achieved by Anderson.
U.S. Pat. Appl. Pub. No. 20060010027 of Redman (hereinafter Redman) disclosed a system for determining movement of customers to analyze customers' decisions and optimize product presentation with traffic pattern analysis. Redman noted that the observation of the traffic pattern can be processed by a manual observation or observation with cameras and facial recognition technology, which could record customers' faces. However, in a preferred embodiment, Redman used a RFID tag and a tag reader system to track the traffic patterns of customers.
Although Redman disclosed an idea of using cameras to observe customers' traffic patterns, this cannot be employed to track the actual decision-making process of shoppers in front of a category as discussed in the present invention. Redman is foreign to the idea of the detailed processes in the present invention for obtaining the behavioral data. Especially, Redman is further foreign to the detailed methods of accumulating unaided hierarchical decision paths as explained in the present invention.
U.S. Pat. Appl. Pub. No. 20080306756 of Sorensen, et al. (hereinafter Sorensen 20080306756) disclosed a shopper view tracking and analysis system. Sorensen 20080306756 is a system that tracks the views of an active, in-store shopper. Sorensen 20080306756 covers a device that is essentially a head-mounted camera, which moves with the shopper's view, in order to pinpoint what the shopper is looking at. Sorensen 20080306756 also covers analytical techniques to fully capture and statistically assess the view data generated by the shopper's head camera. These techniques could be used to create such measures as the average focal point of a typical shopper or the average “looking” time for any category within the store.
Sorensen 20080306756 does not teach a technique to develop the hierarchical decision process of shoppers in front of product categories. Sorensen 20080306756 is an exemplary method in the prior arts that requires shoppers' involvement in gathering the data about the shopping experience in a cumbersome manner. In the methods of Sorensen 20080306756, (i) the evaluation can be biased, leading to faulty conclusions about the decision process, since the respondents are part of a recruited panel, and (ii) the sample size is limited because of the cost involved in acquiring information, thus providing inaccurate information on shopper behavior in front of a category. On the contrary, the present invention can obtain information about the hierarchical decision process of shoppers in an unaided manner, based on the observation of video-based data. Further, the data obtained by the present invention will be robust, since the system is capturing shopper behavior from a large number of shoppers who shop the category of interest during a predefined period of time rather than obtaining the information from a recruited sample, which can be subjective.
U.S. Pat. Appl. Pub. No. 20060200378 of Sorensen (hereinafter Sorensen 20060200378) disclosed a purchase selection behavior analysis system and method. By using a wireless tracking system, Sorensen 20060200378 consists of a market research method that follows a shopper traveling through a store. Predetermined products are also equipped with devices, so aspects of the shopping experience are recorded, such as shopping time in front of displays, distance from displays, and various angles of the shopper's view in relation to displays. Specific time interval analyses are also included in Sorensen 20060200378, which cover the aforementioned aspects of the shopper experience. Sorensen 20060200378 can only be used for determining shopper exposure to display(s) but cannot be applied to identify the decision process in front of the display, even more for in a particular product category, as defined by the present invention.
Sorensen 20060200378 is foreign to the idea of constructing a decision tree based on the accumulated decision paths that are calculated based on an analysis of captured video images. Furthermore, Sorensen 20060200378 is foreign to the idea of constructing a decision tree based on the accumulated shopping interaction, observed by a video analytic system, in front of product categories as disclosed in the present invention, such as based on hand movement tracking.
U.S. Pat. Appl. Pub. No. 20060010030 of Sorensen (hereinafter Sorensen 20060010030) disclosed a system and method for modeling shopping behavior. Sorensen 20060010030 takes shopper path data, product position data, and a general store map, and attempts to model the shopping behavior in different store layouts. As Sorensen 20060010030 discusses, shopper behavior, such as an expected shopper flow and expected purchasing patterns, can be predicted using average relationships between traffic points and product points, based upon the positions of the products and the behavioral domains within a specific store. The intention of the prior art is to normalize shopper behavior in a set of stores with different layouts and to model expected traffic flow and purchase patterns. However, Sorensen 20060010030 cannot be employed for developing the hierarchical decision process of shoppers in front of a product category, as described in the current invention. Sorensen 20060010030 is foreign to the idea of constructing a decision tree based on the accumulated decision paths that are calculated based on an analysis of captured video images and the accumulated shopping interaction, observed by a video analytic system, in front of product categories, as disclosed in the present invention.
Further, Sorensen 2006010030 utilizes RFID and infrared tracking systems for obtaining shopper path data. These techniques are not effective, since they are dependent on the shoppers traveling with carts while in the store; in instances when the shoppers leave the cart at the end of an aisle or in the periphery to shop a category in an aisle, the data obtained from the system will be judgmental and incorrect. Also, Sorensen 2006010030 is foreign to the idea of employing computer vision-based technology, as described in the present invention, to automatically track shopper behavior in front of a category for developing the decision trees.
U.S. Pat. Appl. Pub. No. 20010014868 of Herz, et al. (hereinafter Herz) disclosed a system for the automatic determination of customized prices and promotions. Herz is an online system for presenting online shoppers with customized prices and promotions. The system automatically constructs and updates profiles of shoppers based on their demographics and their history of shopping behavior, which includes both their purchases and their requests for, or reactions to, product information. A shopper's behavior in response to various possible product offers is then predicted by considering how those shoppers with the most similar profiles have behaved with respect to the most similar offers. The system then constructs customized prices and promotions for that shopper.
Herz is only focused on customizing prices and promotions for an online shopper by predicting their behavior and cannot be applied for understanding the decision process of shoppers in a physical retail environment, as detailed in the present invention. Further, the usage of video analytics techniques to determine the decision process of shoppers in a retail environment is foreign to Herz.